Machine Learning Operations Engineer

Vintti is a staffing agency that leverages the geographical advantage of Latin America to benefit US businesses. We connect companies with professionals who work in time zones closely aligned with or identical to US hours, ensuring seamless communication and collaboration. This synchronicity allows for real-time interaction, enhancing productivity and eliminating the delays often associated with offshore staffing.

A Machine Learning Operations Engineer, often referred to as an MLOps Engineer, plays a critical role in bridging the gap between data science and IT operations by deploying, monitoring, and optimizing machine learning models in production. This role focuses on the end-to-end lifecycle of machine learning models, including data preprocessing, model training, deployment, and ongoing maintenance. MLOps Engineers are responsible for creating scalable and reliable pipelines, ensuring model performance and accuracy, and automating repetitive tasks. Their work ensures that machine learning models can be seamlessly integrated into business processes, providing continuous value and insights from data.

- Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field
- Proven experience in machine learning, data science, or software engineering
- Proficiency in programming languages such as Python, Java, or C++
- Strong understanding of machine learning algorithms and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
- Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) for deploying ML models
- Knowledge of CI/CD practices and tools (e.g., Jenkins, GitLab CI, CircleCI)
- Experience with data processing frameworks (e.g., Apache Spark, Hadoop)
- Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes)
- Proficient in version control systems (e.g., Git)
- Strong problem-solving and troubleshooting skills
- Experience with monitoring and alerting tools (e.g., Prometheus, Grafana)
- Knowledge of big data storage solutions (e.g., SQL databases, NoSQL databases, data lakes)
- Understanding of data privacy policies and compliance regulations (e.g., GDPR, CCPA)
- Excellent communication and collaboration skills
- Ability to conduct statistical analysis and interpret model performance metrics
- Familiarity with software development lifecycle (SDLC) and Agile methodologies
- Experience with automating workflows and scripting
- Knowledge of infrastructure as code (IaC) tools (e.g., Terraform, Ansible)
- Capacity to stay updated with the latest trends and advancements in ML and MLOps
- Strong organizational skills and attention to detail
- Ability to work under pressure and manage multiple tasks simultaneously
- Proven ability to provide technical guidance and support to team members

- Monitor and maintain machine learning model performance and health in production environments
- Develop, test, and deploy machine learning pipelines in production systems
- Create and maintain documentation of ML models, processes, and workflows
- Troubleshoot and resolve issues related to data processing, model training, and deployment
- Collaborate with data scientists, software engineers, and team members for ML model integration
- Implement CI/CD practices for ML model deployment
- Ensure data privacy and compliance with company policies and regulations
- Automate routine tasks to enhance workflow efficiency
- Conduct regular code reviews and provide feedback to team members
- Evaluate and implement new tools and frameworks for ML infrastructure improvement
- Manage and maintain large-scale data storage solutions
- Optimize model performance and scalability for production
- Coordinate with operations teams for system reliability and availability
- Perform statistical analysis and visualize model performance metrics
- Stay updated with industry trends and technologies in ML and operations
- Design and implement monitoring and alerting systems for production models
- Provide technical support and guidance for onboarding new ML models and tools
- Schedule and execute model retraining and updates based on performance and new data
- Ensure efficient utilization of computational resources for model training and inference
- Assist in managing and provisioning cloud infrastructure for ML operations

The ideal candidate for the Machine Learning Operations Engineer role is a highly analytical and results-driven professional with a strong foundation in machine learning and data science concepts, complemented by extensive experience in software engineering. They demonstrate proficiency in programming languages such as Python and Java and possess expert knowledge of ML frameworks, particularly TensorFlow, PyTorch, and Scikit-learn. Their expertise extends to cloud platforms like AWS, Google Cloud, and Azure, where they have successfully deployed and managed ML models. The candidate is well-versed in CI/CD practices and tools, showcasing their ability to streamline and automate machine learning pipelines efficiently. Their familiarity with containerization tools like Docker and Kubernetes and data processing frameworks such as Apache Spark and Hadoop ensures they can handle complex data and ML operations seamlessly. Skilled in utilizing version control systems like Git, they maintain the integrity and consistency of codebases. Their strong troubleshooting skills enable them to resolve intricate data and ML model issues swiftly. Experienced with monitoring and alerting tools such as Prometheus and Grafana, they ensure the robust performance of models in production. They possess in-depth knowledge of both SQL and NoSQL big data storage solutions and are well-versed in data privacy and compliance regulations like GDPR and CCPA. Their excellent communication and collaboration skills facilitate effective teamwork, while their capability to perform and interpret statistical analysis aids in optimizing model performance. Familiar with Agile methodologies and SDLC, they adeptly manage workflow efficiency through scripting and automation. Proficient in infrastructure as code tools like Terraform and Ansible, they ensure the scalability and reliability of ML operations. They are committed to staying current with industry trends and advancements, showcasing high organizational skills and meticulous attention to detail. Under pressure, they manage multiple tasks efficiently, demonstrating a proven ability to guide and support team members while continually learning and adapting to new tools and technologies.

- Monitor machine learning model performance and health in production environments
- Develop, test, and deploy machine learning pipelines within production systems
- Create and maintain detailed documentation of ML models, processes, and workflows
- Troubleshoot and resolve issues related to data processing, model training, and deployment
- Collaborate with data scientists, software engineers, and other team members to integrate ML models into applications
- Implement CI/CD practices for deploying ML models
- Ensure data privacy and compliance with company policies and regulations
- Automate routine tasks to enhance workflow efficiency
- Conduct regular code reviews and provide constructive feedback to team members
- Evaluate and implement new tools and frameworks to improve ML infrastructure
- Manage and maintain large-scale data storage solutions
- Optimize model performance and scalability for production environments
- Coordinate with operations teams to ensure 24/7 system reliability and availability
- Perform statistical analysis and visualize model performance metrics
- Stay updated with the latest industry trends and technologies in machine learning and operations
- Design and implement monitoring and alerting systems for models in production
- Provide technical support and guidance for onboarding new ML models and tools
- Schedule and execute model retraining and updates based on performance metrics and new data
- Ensure efficient utilization of computational resources for model training and inference
- Assist in managing and provisioning cloud infrastructure related to ML operations

- Strong analytical and problem-solving mindset
- In-depth knowledge of machine learning and data science concepts
- Highly proficient in programming languages like Python and Java
- Expertise in ML frameworks such as TensorFlow, PyTorch, and Scikit-learn
- Proficient with cloud platforms like AWS, Google Cloud, and Azure
- Experience implementing CI/CD practices and tools
- Familiarity with containerization tools like Docker and Kubernetes
- Skilled in data processing frameworks like Apache Spark and Hadoop
- Proficient in version control systems like Git
- Capable of troubleshooting complex data and ML model issues
- Experienced with monitoring and alerting tools such as Prometheus and Grafana
- Understanding of big data storage solutions, both SQL and NoSQL
- In-depth knowledge of data privacy and compliance regulations
- Strong communication and collaboration abilities
- Capable of performing and interpreting statistical analysis
- Familiar with Agile methodologies and SDLC
- Skilled in scripting and automating workflows
- Knowledgeable in infrastructure as code tools like Terraform and Ansible
- Able to stay current with industry trends and advancements
- High level of organizational skills and attention to detail
- Ability to handle pressure and manage multiple tasks efficiently
- Demonstrated ability to guide and support team members
- Eagerness to learn and adapt to new tools and technologies

- Competitive salary range based on experience and qualifications
- Health, dental, and vision insurance
- Flexible working hours
- Remote work options
- Generous paid time off and holidays
- 401(k) retirement savings plan with company match
- Professional development opportunities
- Access to training programs and conferences
- Career advancement opportunities
- Wellness programs and resources
- Employee assistance programs (EAP)
- Company-sponsored social events and activities
- Relocation assistance (if applicable)
- On-site gym or fitness reimbursement
- Commuter benefits or transportation reimbursement
- Equity or stock option plans
- Inclusive and diverse work environment
- Enhanced parental leave policies
- Access to the latest tools and technologies
- Collaborative and innovative team culture